Based on the translational models [1]}, [2]}, [3]}, [4]}, [5]},
we first set up a search space that can cover all these translational models as special cases.
Then,
we conduct random search in this space to explore new designs of scoring functions.
After evaluating 10 SFs,
we identified a weird SF which does not contain head entity embedding
but outperforms the existing models.
This is abnormal as triplets in KGs are formed by head entity, relation and tail entity.
By analyzing the distribution of tail entities,
we guess the success of such a weird SF may attribute to the
concentrated distribution and the evaluation protocol
with random negative sampling.
